Data mining process using clustering: a survey

Saraee, MH; Ahmadian, N; Narimani, Z

Abstract

Clustering is a basic and useful method in
understanding and exploring a data set.
Clustering is division of data into groups of
similar objects. Each group, called cluster,
consists of objects that are similar between
themselves and dissimilar to objects of other
groups. Interest in clustering has increased
recently in new areas of applications
including data mining, bioinformatics, web
mining, text mining, image analysis and so
on. This survey focuses on clustering in data
mining.
The goal of this survey is to provide a
review of different clustering algorithms in
data mining. A Categorization of clustering
algorithms has been provided closely
followed by this survey. The basics of
Hierarchical Clustering include Linkage
Metrics, Hierarchical Clusters of Arbitrary
and Binary Divisive Partitioning is
discussed at first. Next discussion is
Algorithms of the Partitioning Relocation
Clustering include Probabilistic Clustering,
K-Medoids Methods, K-Means Methods.
Density-Based-Partitioning, Grid-Based
Methods and Co-Occurrence of Categorical
Data are other sections. Their comparisons
are mostly based on some specific
applications and under certain conditions. So
the results may become quite different if the
conditions change.

Citation

Saraee, M., Ahmadian, N., & Narimani, Z. (2007, November). Data mining process using clustering: a survey. Presented at IDMC'07, Tehran, Iran

• Presentation Conference Type: Other
• Conference Name: IDMC'07
• Conference Location: Tehran, Iran
• Start Date: Nov 20, 2007
• End Date: Nov 21, 2007
• Deposit Date: Nov 9, 2011
• Publicly Available Date: Apr 5, 2016
• Additional Information: Event Type : Conference